Thinking Mode in Qwen3 Enhances Multi-Step Reasoning on SWE-Bench Verified

This report synthesises findings from 4 peer-reviewed papers addressing the following research question: To what extent does the thinking mode in Qwen3 improve performance on multi-step reasoning tasks in SWE-bench Verified compared to non-thinking mode, and how does this trade-off affect inference. Small language models are attractive for production deployment due to their low cost, fast inference, and ease of specialization. However, adapting them to a specific task remains a challenging engineering loop, driven not by training itself but by surrounding decisions: data. 7 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: To what extent does the thinking mode in Qwen3 improve performance on multi-step reasoning tasks in SWE-bench Verified compared to non-thinking mode, and how does this trade-off affect inference latency?

Autonomous literature synthesis. Automated review score: 8.3/10. Full text and citation available at Assignee Research.